4 Answers
4

If you want a generic mechanical approach, try to build a time-series model. A log-linear trend model or building an auto-regressive model such as ARMA (on a stationary) series would probably work. As a start point I would plot the data to see what features you notice (seasonality? exponential growth? mean-reversion? trend? one-off shocks?)

Note that securities analysts forecasts are better than these time-series models. Also these time-series models would have limited ability to extrapolate beyond a couple k-step ahead periods. So if you need cumulative out-of-sample earnings in years and your training data is in months, you will have low confidence in your prediction.

There is a rich academic history of using such models used to forecast quarterly earnings although that doesn't mean it's a great trading strategy.

Most of these models would be very industry specific, but there is never going to be a default way to estimate earnings. Banks are very descriptive of these earnings models. I would start with an industry report and go from there.

This is a serious task to do for one company. Much more so than I think you realize.

The most common model is...."Next year's earnings will be this year's earnings plus some percentage (an exponential trend)". Why?

Look closely at the process that generates an earnings number.

Earnings = Sales - Costs

Let's start off by assuming that you can estimate Sales with 100% accuracy. And, let's assume that you can estimate Costs within +/- 5% (not likely, but let's use this anyway). If Costs are typically 90% of Sales (a 10% profit margin), then the +/- 5% error in Costs gives you an Earnings Estimate that is 5% to 15% of Sales. That's an Earnings spread based on Earnings of -50% to +50% (for a +/-5% error in Costs). As you can imagine, a higher uncertainty in Costs plus the uncertainty in Sales can drive that Earnings Estimate spread to some really big numbers.

So, it turns out that you'll be lucky if you can estimate an Earnings exponential trend.

One other thing to keep in mind. The smaller the company, the more ridiculous the Earnings Estimates. And, estimating the Earnings of the S&P500 beyond an exponential trend, the easiest of all estimates, is by no means easy.

If you want to try some schemes on various companies, here's some data:

I agree with @Quant Guy's answer. His approach should definitely be the first thing you try, and you should also heed his warning regarding the low confidence when forecasting out many years.

I would only add that if you have data for more companies available, even if your goal is to only estimate the earnings for one company, you can improve your estimate further with a vector auto-regression (VAR). Better yet, you can estimate relationships between this firm and its industry average earnings and add a term for convergence of this firm's growth rate to the industry average. ARMA estimates will also probably be better on aggregates such as industries/sectors.